Update app.py

app.py

@@ -1,122 +1,55 @@
-from transformers import CLIPSegProcessor, CLIPSegForImageSegmentation
-import gradio as gr
-from PIL import Image
-import torch
-import matplotlib.pyplot as plt
-import cv2
-import torch
-import numpy as np
-
-processor = CLIPSegProcessor.from_pretrained("CIDAS/clipseg-rd64-refined")
-model = CLIPSegForImageSegmentation.from_pretrained("CIDAS/clipseg-rd64-refined")
-
-
-def process_image(image, prompt, threhsold, alpha_value, draw_rectangles):
-    inputs = processor(
-        text=prompt, images=image, padding="max_length", return_tensors="pt"
-    )
-
-    # predict
-    with torch.no_grad():
-        outputs = model(**inputs)
-        preds = outputs.logits
-
-    pred = torch.sigmoid(preds)
-    mat = pred.cpu().numpy()
-    mask = Image.fromarray(np.uint8(mat * 255), "L")
-    mask = mask.convert("RGB")
-    mask = mask.resize(image.size)
-    mask = np.array(mask)[:, :, 0]
-
-    # normalize the mask
-    mask_min = mask.min()
-    mask_max = mask.max()
-    mask = (mask - mask_min) / (mask_max - mask_min)
-
-    # threshold the mask
-    bmask = mask > threhsold
-    # zero out values below the threshold
-    mask[mask < threhsold] = 0
-
-    fig, ax = plt.subplots()
-    ax.imshow(image)
-    ax.imshow(mask, alpha=alpha_value, cmap="jet")
-
-    if draw_rectangles:
-        contours, hierarchy = cv2.findContours(
-            bmask.astype(np.uint8), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE
-        )
-        for contour in contours:
-            x, y, w, h = cv2.boundingRect(contour)
-            rect = plt.Rectangle(
-                (x, y), w, h, fill=False, edgecolor="yellow", linewidth=2
-            )
-            ax.add_patch(rect)
-
-    ax.axis("off")
-    plt.tight_layout()
-
-    bmask = Image.fromarray(bmask.astype(np.uint8) * 255, "L")
-    output_image = Image.new("RGBA", image.size, (0, 0, 0, 0))
-    output_image.paste(image, mask=bmask)
-
-    return fig, mask, output_image
-
-
-title = "Interactive demo: zero-shot image segmentation with CLIPSeg"
-description = "Demo for using CLIPSeg, a CLIP-based model for zero- and one-shot image segmentation. To use it, simply upload an image and add a text to mask (identify in the image), or use one of the examples below and click 'submit'. Results will show up in a few seconds."
-article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2112.10003'>CLIPSeg: Image Segmentation Using Text and Image Prompts</a> | <a href='https://huggingface.co/docs/transformers/main/en/model_doc/clipseg'>HuggingFace docs</a></p>"
-
-
-with gr.Blocks() as demo:
-    gr.Markdown("# CLIPSeg: Image Segmentation Using Text and Image Prompts")
-    gr.Markdown(article)
-    gr.Markdown(description)
-    gr.Markdown(
-        "*Example images are taken from the [ImageNet-A](https://paperswithcode.com/dataset/imagenet-a) dataset*"
-    )
-
-    with gr.Row():
-        with gr.Column():
-            input_image = gr.Image(type="pil")
-            input_prompt = gr.Textbox(label="Please describe what you want to identify")
-            input_slider_T = gr.Slider(
-                minimum=0, maximum=1, value=0.4, label="Threshold"
-            )
-            input_slider_A = gr.Slider(minimum=0, maximum=1, value=0.5, label="Alpha")
-            draw_rectangles = gr.Checkbox(label="Draw rectangles")
-            btn_process = gr.Button(label="Process")
-
-        with gr.Column():
-            output_plot = gr.Plot(label="Segmentation Result")
-            output_mask = gr.Image(label="Mask")
-            output_image = gr.Image(label="Output Image")
-
-    btn_process.click(
-        process_image,
-        inputs=[
-            input_image,
-            input_prompt,
-            input_slider_T,
-            input_slider_A,
-            draw_rectangles,
-        ],
-        outputs=[output_plot, output_mask, output_image],
-    )
-
-    gr.Examples(
-        [
-            ["0.003473_cliff _ cliff_0.51112.jpg", "dog", 0.5, 0.5, True],
-            ["0.001861_submarine _ submarine_0.9862991.jpg", "beacon", 0.55, 0.4, True],
-            ["0.004658_spatula _ spatula_0.35416836.jpg", "banana", 0.4, 0.5, True],
-        ],
-        inputs=[
-            input_image,
-            input_prompt,
-            input_slider_T,
-            input_slider_A,
-            draw_rectangles,
-        ],
-    )
-
-demo.launch()
+from flask import Flask, request, send_from_directory
+import os
+
+app = Flask(__name__)
+
+UPLOAD_FOLDER = 'uploads'
+if not os.path.exists(UPLOAD_FOLDER):
+    os.makedirs(UPLOAD_FOLDER)
+
+app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
+
+@app.route('/', methods=['GET', 'POST'])
+def upload_file():
+    if request.method == 'POST':
+        if 'file' not in request.files:
+            return 'No file part'
+        
+        file = request.files['file']
+        
+        if file.filename == '':
+            return 'No selected file'
+        
+        if file:
+            filename = file.filename
+            file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename))
+            return 'File uploaded successfully'
+    
+    return '''
+    <!doctype html>
+    <title>Upload new File</title>
+    <h1>Upload new File</h1>
+    <form method=post enctype=multipart/form-data>
+      <input type=file name=file>
+      <input type=submit value=Upload>
+    </form>
+    '''
+
+@app.route('/files')
+def list_files():
+    files = os.listdir(app.config['UPLOAD_FOLDER'])
+    return '''
+    <!doctype html>
+    <title>Uploaded files</title>
+    <h1>Uploaded files</h1>
+    <ul>
+    ''' + ''.join(['<li><a href="/download/{}">{}</a></li>'.format(f, f) for f in files]) + '''
+    </ul>
+    '''
+
+@app.route('/download/<filename>')
+def download_file(filename):
+    return send_from_directory(app.config['UPLOAD_FOLDER'], filename, as_attachment=True)
+
+if __name__ == '__main__':
+     app.run(debug=True, port=7860)